1018 papers
Bioinformatics sits at the exciting intersection where biology meets data science, using powerful computer tools to decode the vast complexity of living systems. From mapping the human genome to tracking how viruses evolve, this field transforms raw biological information into actionable insights that drive modern medicine and research forward without requiring a supercomputer to understand the basics.
On Gist.Science, we ensure you never miss a breakthrough by processing every new preprint in this category directly from bioRxiv. Our team provides both plain-language explanations and detailed technical summaries for each paper, making cutting-edge discoveries accessible to everyone regardless of their background.
Below are the latest bioinformatics papers added from bioRxiv, ready for you to explore with clarity and depth.
The paper introduces RigidSSL, a rigidity-aware self-supervised learning framework that leverages geometric pretraining on static structures and molecular dynamics trajectories to significantly enhance protein designability, novelty, and the modeling of biophysically realistic conformational ensembles.
ProtNHF is a generative model that leverages neural Hamiltonian flows and an additive energy structure to enable controllable protein sequence generation with continuous, quantitative control over properties like amino acid composition and net charge through inference-time analytical bias functions, eliminating the need for retraining or architectural modifications.
The paper introduces CellSweep, an efficient and effective tool that outperforms existing methods in removing ambient and bulk contamination from single-cell genomics data to ensure accurate downstream analysis.
This study introduces a splitting-and-merging approach that extends previous fixed-window methods to allow for variable window sizes across whole genome alignments, demonstrating improved accuracy in recovering gene tree topologies on simulated data and providing nuanced insights into recombination-driven variation in *Heliconius* butterflies and great apes.
The paper introduces SLiMMine, a deep learning-based web server that utilizes refined annotations and protein embeddings to accurately predict known short linear motifs (SLiMs) in the human proteome, significantly reducing false positives and enabling the discovery of novel SLiMs and specific protein-protein interactions.
The paper introduces Latent Space Dynamics (LSD), a thermodynamics-inspired framework that utilizes neural ordinary differential equations to model cell differentiation as evolution on a learned Waddington landscape, enabling accurate trajectory reconstruction, fate prediction, and quantitative analysis of cellular plasticity through a differentiable potential function and local entropy.
This paper presents a GPU-accelerated version of kallisto that achieves a 30-50x speedup over its CPU counterpart by redesigning core algorithms for massive parallelism, enabling RNA-seq transcript quantification to be completed in seconds rather than minutes.
This study demonstrates that advanced peptide-spectrum match rescoring strategies significantly harmonize identification outcomes and reduce variability across diverse search engines and datasets, thereby enhancing the robustness and comparability of proteomics analyses while highlighting the continued importance of careful feature selection and database configuration for reliable false discovery rate control.
This study utilizes integrated transcriptome and network analyses to identify shared molecular signatures, including hub genes like TTR, specific transcription factors, and miRNAs, that elucidate the common biological pathways linking Alcohol Use Disorder with various psychiatric conditions and suggest potential therapeutic targets.
This study presents a proof-of-concept framework using single-cell foundation models and a large-scale Perturb-seq dataset to successfully prioritize genetic targets for reversing cellular inflammation, demonstrating that incorporating disease-relevant proinflammatory conditions significantly improves the identification of biologically relevant therapeutic targets compared to basal conditions alone.